TAM×DeployOne
Najm-aligned
Multimodal RAG · Vision · Speech · Language

Who was at fault, and why — decided in seconds

TAM × DeployOne reconstructs collisions from photos, dashcam footage, a driver's voice note and written statements — then apportions liability with regulation-grounded reasoning an adjuster can defend.

See

Damage geometry, skid marks, signals, lane position

Hear

Voice statements transcribed and cross-examined

Judge

Fault split with cited traffic articles

Cases adjudicated

Live ledger

Mean engine confidence

Awaiting first case

Median time to verdict

< 60 sec

vs 11 days manual

Settlement exposure

Across filed cases

Case throughput

Last 7 days

Collision mix

Adjudicate a case to populate the mix

Modality lift

Mean engine confidence by the evidence present in the retrieval context.

No modality data yet

Case ledger

Live

No cases filed yet. Every verdict you run is stored here with a shareable dossier.

Evidence intake

Fuse scene imagery, dashcam video, a spoken statement and written notes into one liability verdict.

Multimodal RAG
Record the driver or witness account

Evidence is fused across vision, speech and language before apportioning fault.

No active reconstruction

Add at least one piece of evidence above. The engine performs best with a wide scene shot, a close-up of the damage, and the driver's own account.

What we do

A liability engine that reads the crash the way a forensic investigator would

Most claims stall because fault is argued from partial evidence — one photo, one account, one adjuster's reading. TAM × DeployOne fuses everything captured at the roadside into a single reasoning pass, and returns an apportionment with the reasoning attached.

Scene & damage vision

Crush geometry, impact angle, resting positions, debris fields, skid marks, lane markings and signal state are read from photos and plate-level close-ups.

Dashcam video reasoning

Footage is sampled into key frames across the pre-impact window so the engine sees the manoeuvre, not just the aftermath.

Voice statement analysis

The driver or witness speaks in their own words. The engine transcribes, weighs and cross-examines the account against the physical evidence.

Written account parsing

Adjuster notes, police summaries and claim text are fused into the same retrieval context as the imagery and audio.

Regulation-grounded verdicts

Every apportionment cites the MOI Traffic Law article or Najm schedule row it rests on, so the decision is defensible on appeal.

Adversarial stress-test

One click builds the opposing driver's strongest rebuttal and tells you whether it would actually move the fault split.

How a case moves

01

Ingest

Photos, dashcam clips, a voice note and written notes land in one intake. Video is auto-sampled; audio is captured in-browser.

02

Fuse

Multimodal RAG puts every modality into a single retrieval context alongside Saudi traffic regulation, so signals corroborate or contradict each other.

03

Reconstruct

The engine rebuilds the second-by-second sequence, names the manoeuvre error and explains why the mistake was made.

04

Adjudicate

Fault is apportioned to 100%, scored for confidence, priced for settlement and filed as a shareable dossier you can interrogate.